Disambiguation of term meaning

ABSTRACT

A meaning of a term is determined using the contents of a corpus of books through use of metadata about the books within the corpus, terms in the same work which provide context, and so forth. Users may query to determine the meaning of a term. Users may also build vocabulary skills by testing as well. A changing meaning of a term over time may be determined and utilized as well. Searches are facilitated by the enhanced ability to determine meaning of the terms, particularly in context. Feedback from the searches may also be used to refine future searches.

BACKGROUND

Many people are consuming books in digital form through use of a widevariety of electronic devices. Among these electronic devices areelectronic book reader devices, cellular telephones, personal digitalassistants (PDAs), tablets, netbooks, and the like. As more electronicbooks (or “eBooks”) become available, more readers are opting to readbooks in digital form over the traditional paper-based form. Consumptionof eBooks on electronic devices affords more opportunities to observehow readers interact with the content. One key opportunity is to betterunderstand readers' knowledge of terms and to find ways to improvereader comprehension.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is set forth with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Theuse of the same reference numbers in different figures indicates similaror identical items or features.

FIG. 1 illustrates an architecture in which terms from variouselectronic books (eBooks) deemed to be of interest to readers arecollected by reader devices and aggregated at a remote service.

FIG. 2 illustrates metadata which may be used by the architecture ofFIG. 1.

FIG. 3 illustrates an architecture in which vocabulary questions aregenerated based on the terms of interest collected in FIG. 1 to test thereaders' comprehension of those terms.

FIG. 4 shows an electronic book reader device with a user interface thatpresents a vocabulary question for a particular vocabulary term.

FIG. 5 shows the electronic book reader device with a user interfacethat presents results to the reader's response to the question in FIG.4.

FIG. 6 shows the electronic book reader device with a user interfacethat reveals the eBook passage from which the particular vocabulary termwas extracted.

FIG. 7 shows the electronic book reader device with a user interfacethat allows the reader to rate the vocabulary question in FIG. 4.

FIGS. 8-11 illustrate an architecture in which the vocabulary serviceprovides more extensive information pertaining to the vocabulary termsthat the reader has indicated an interest in learning, as well as termsof interest to the entire population of readers. Each of the figuresillustrates a different screen rendering of a user interface forproviding the vocabulary information from different perspectives.

FIG. 12 illustrates an architecture in which the vocabulary serviceprovides definitions and information about a term.

FIG. 13 illustrates an architecture for analyzing the changes of a termover time based upon data in a corpus of content items.

FIG. 14 illustrates an architecture for providing examples of a selectedvocabulary term, the examples having been drawn from the corpus ofcontent items.

FIG. 15 illustrates an architecture for enhancing a search by using adetermined meaning of the search term.

FIGS. 16-17 illustrate an architecture in which the vocabulary servicesupports a network-based vocabulary game including a confounder game.

FIG. 18 is a block diagram illustrating selected modules in a computingsystem that implements determination of meaning of terms and generationof the vocabulary questions for those terms.

FIG. 19 is a flow diagram for a general process of accepting a queryterm and presenting context-appropriate samples of the query term.

FIG. 20 is a flow diagram for a general process of determining a meaningof a selected query term at least in part due to a correspondencebetween metadata of the content in which the selected term resides andthe metadata of the query term.

DETAILED DESCRIPTION

This disclosure describes enhancing a user's interaction with terms incontent items such as electronic books (eBooks). The user initiates aquery for a definition of a term such as a word or expression comprisinga plurality of words. Based at least in part upon the context of theterm, the resulting definition may be automatically disambiguated toprovide a contextually accurate meaning. The disambiguation is aided, atleast in part, by analyzing metadata associated with the content item,the term, the user, and so forth, as well as the content item in whichthe term and associated words are contained. Terms of interest may bestored and used to generate vocabulary questions to test the readers'comprehension of the terms.

A term may be deemed of interest when a reader requests a definition,highlights the term, annotates the term, or otherwise affirmativelyinteracts with the term through an electronic device on which the eBookis being consumed. The terms are captured by the electronic device asthe reader consumes the eBook and stored locally and/or uploaded to aremote service from time-to-time.

In addition to a definition, a user may view examples of the term inexcerpts from the corpus. Thus, the user sees the term in actual usageand is thus better able to build her own comprehension of the term.

Vocabulary questions test the readers understanding of the terms,particularly in the context of the eBooks from which the terms wereextracted. The vocabulary questions may be crafted in any number offormats, such as true/false, open ended, or multiple-choice thatincludes the correct answer and one or more confounders. The vocabularyterms may be provided in the question, or as one of the possibleanswers. The vocabulary questions may be generated by users,automatically, or through other techniques, such as through use of amechanical solutions network. The vocabulary questions may also becreated by the electronic device or by the remote service.

The vocabulary questions are subsequently presented to the reader forconsideration. The reader may enter responses to the questions, and theaccuracy can be tracked and reported. Furthermore, as part of thefeedback, the excerpts from the eBook, which contain the terms andformed the basis for the vocabulary questions, may be presented to thereader to provide the context and reinforce the learning process. Thereader may be retested from time-to-time to evaluate retention of thevocabulary terms.

The vocabulary questions may further be evaluated by the reader. Afterlearning the intended correct answer, the reader may be given anopportunity to comment on whether the vocabulary question wasappropriate, inaccurate, correct, and so forth. In some instances, thevocabulary questions may be rated by a community of readers, leading toa “Wisdom of Crowds” effect where certain questions become morefavorable than others for testing comprehension. Discussion forums mayfurther be provided to facilitate community discussion of the vocabularyterms, questions, and answers, as well as other social dialog regardingthe book.

In addition to building vocabulary through questions, discussion, and soforth, users may also have terms disambiguated. Different meanings for aterm in a passage may be distinguished by ascertaining the period inwhich the term was used, social/cultural context, contents of thepassage in which the term appears, and so forth. In addition to thevocabulary training discussed above, disambiguating a term is useful ina variety of situations. For example, students benefit from seeing useof the term in excerpts which are contemporaneous with a passage understudy and have substantially the same meaning. Also, disambiguation whenused against a corpus of data provides insight as to how the meaning ofa term changes over time.

Furthermore, disambiguation of meaning improves searches by determiningan intended meaning. For example, search terms are disambiguated to finda particular meaning of a term given the context in which the searchterm is used. Because many languages contain terms which may have manydivergent (if not mutually exclusive) meanings depending upon their use,determining the intended meaning and searching for the intended meaningimproves the relevance of search results.

For discussion purposes, the following discussion provides an exemplaryenvironment in which vocabulary building may be implemented in thecontext of readers consuming eBooks via electronic devices. The terms“electronic book” and/or “eBook”, as used herein, include electronic ordigital representations of printed works, as well as digital contentthat may include text, multimedia, hypertext, and/or hypermedia.Examples of printed and/or digital works include, but are not limitedto, books, magazines, newspapers, periodicals, journals, referencematerials, telephone books, textbooks, anthologies, instruction manuals,proceedings of meetings, forms, directories, maps, web pages, etc.However, certain concepts described herein are also applicable to othertypes of digital content items, such as music, audio books, video, andother content items that people watch, listens to, or otherwiseexperience.

Architectural Environment

FIG. 1 illustrates an example architecture 100 in which terms deemed tobe of interest to readers during consumption of eBooks are collected byreader devices and aggregated at a remote service. Many readers 102(1),. . . , 102(N) are illustrated in FIG. 1 as belonging to a readerpopulation 104. As shown, each reader 102(1)-(N) employs a correspondingelectronic device 106(1), . . . , 106(D) to consume one or more eBooks.The electronic devices 106(1)-(D), or generally devices 106, are eachcapable of rendering, playing, or otherwise presenting the eBook orother content items. In this illustration, the electronic devices areembodied as a first type of eBook reader device 106(1), a multifunctioncommunication device 106(2), and a second type of eBook reader device106(D). While eBook reader devices and a communication device areillustrated, other types of electronic devices may be used to rendereBooks, such as portable digital assistants (PDAs), cellular telephones,portable media players, tablet computers, netbooks, notebooks, desktopcomputers, and the like.

The first reader 102(1) employs the eBook reader device 106(1) to renderan eBook version of William Shakespeare's classic, Romeo and Juliet. Aportion of the eBook 108 is presented on the device's display 110. Here,the eBook portion 108 is the famous passage:

-   -   But, soft! What light through yonder window breaks! It is the        east, and Juliet is the sun!—Arise, fair sun, and kill the        envious moon, Who is already sick and pale with grief . . . .

Similarly, a second reader 102(N) employs the eBook reader device 106(D)to render the following portion 112 of an eBook entitled Renaissance Dayon the device's display 114:

-   -   To my right was the King's Pavilion, while on the left, tables        of ale and goods to be had for a parsimonious sum. It was        perhaps the most exquisite fair I have ever seen.

Each electronic device 106(1)-(D) provides controls (e.g., hardwareand/or software controls) that enable a corresponding reader 102(1)-(N)to interact with the eBook during consumption. For instance, the devices106 allow readers to select eBooks, turn pages, look up termdefinitions, highlight sections, annotate the work, access remote bookstores, and so forth. Of particular interest are user interactions thatcan be interpreted as a reader's interest in one or more terms. Thisinterest may be exhibited through requesting a definition, highlightingthe term, adding a comment pertaining to the term, and so forth.

For instance, while reading the famous passage 108 displayed on eBookreader device 106(1), the reader 102(1) would like to better understandthe use of the term “fair” in this particular work. Accordingly, thereader 102(1) uses the controls on the eBook reader device 106(1) toemphasize or otherwise select the term “fair” on the display 110, asindicated by the underlined term. In response, a definition box 116 isdepicted at a bottom portion of the display 110 and the definition ofthe term “fair” as “flawless” is provided in the box 116. The definitionengine may be invoked through express selection and entry of the term“fair” or by simply emphasizing the term in the text by hovering apointer over the term, moving a cursor to the term, or through other UIselection techniques. In some implementations, the genre or context ofthe work may be considered when choosing among the definitions toprovide to the reader. In this example, the term “fair” is extractedfrom the medieval work Romeo and Juliet, and proximate to the archaicterm “yonder”. Based on this information, the engine may order thedefinitions with the more likely selection of “fair” as pertaining tobeauty—rather than weather, bias, or an event—ordered first.

Similarly, the second reader 102(N) might become curious about the term“fair” when passage 112 is displayed on the eBook reader device 106(D).Upon selecting the term “fair”, a definition box 118 containing adefinition of the term “fair” as “a gathering of buyers and sellers” ispresented at the bottom of the display 114. Because of the differentcontext present within the two passages 108 and 112, differentdefinitions for the same term were provided which are contextappropriate. For example, with regards to passage 108, various factorsmay be used to establish the context appropriate meaning of the term“fair.” For example, the proximity of “fair” to the terms “yonder” aswell as metadata of the book, such as the date of publication, may belead to a correspondence within the definition engine of the use in thispassage with the more archaic form indicating “flawless.”

Similarly, the definition 118 of fair as a gathering of buyers andsellers may be ascertained based on the context in which that termappears. For example, the proximity of terms such as “tables”, “goods”and “sum” to “fair” might indicate the topic as being one relating tocommerce, thus corresponding within the definition engine to thedefinition given.

When a reader expresses interest in a term (e.g., seeking a definition,highlighting, annotating, etc.), the term is captured by the eBookreader device and stored in a file. The term is associated with theeBook from which it was extracted, and a unique identifier of the readerand/or device. In some implementations, the eBook reader device may beequipped with a question generator to create vocabulary questions forthe terms captured and stored in the file. The vocabulary questions areintended to test the reader's comprehension of the terms. Such questiongenerators are described below in more detail. However, in otherimplementations, the file with the terms is transferred to a remoteservice for construction of corresponding vocabulary questions.

Accordingly, the electronic reader devices 106(1)-(D) communicate via anetwork 120 to send files of terms to a vocabulary service 122, wherethe terms are stored in association with the eBooks and uniqueidentifiers. The network 120 is representative of any one or combinationof multiple different types of networks, such as the Internet, cablenetworks, wireless networks, and wired networks. In FIG. 1, thevocabulary service 122 is hosted on servers 124(1), 124(2), . . . ,124(S), which receive the terms deemed to be of interest from theelectronic devices 106(1)-(D). The servers 124(1)-(S) have processingand storage capabilities to collect and store terms received from manyreaders in the population 104, and to generate vocabulary questionsbased on the terms. The servers 124(1)-(S) may be embodied in any numberof ways, including as a single server, a cluster of servers, a serverfarm or data center, and so forth, although other server architectures(e.g., a mainframe architecture) may also be used.

The terms collected from the various eBook reader devices are stored ina data structure 126, as represented by the list of terms 128. The termlist 128 includes the term “fair” that was captured by the eBook readerdevice 106(1) and the term “fair” that was captured by the eBook readerdevice 106(D). The terms are stored in association with the eBookidentities 130 from which the terms were extracted, along with uniqueidentifiers (or IDs) 132 that are unique to the readers 102(1)-(N) or toelectronic devices 106. Metadata 134 may also be stored. The metadata isdiscussed below with regards to FIG. 2 in more detail. As shown here,the differing usage of the term “fair” as extracted from differenteBooks (i.e., “ROMJUL” and “RENDAY”), by different readers or devices asindicated by a different IDs associated with these terms.

Through this data structure 126, the vocabulary service 122 may performvarious statistical analyses on the terms. For instance, the vocabularyservice 122 may determine what meaning is to be attributed to a term ina given context, or how the meaning of the term has changed over a giventime, within a genre, and so forth. The vocabulary service 122 may alsodetermine which terms appear most often (i.e., those terms of mostinterest to the readers), or terms that are most frequently looked up,and so forth. The service can further compute these statistics relativeto books, such as the most frequently cited terms in the eBook, or topten books in which the term “fair” is looked up. Furthermore, similarstatistics can be measured relative to the reader or eBook device. Forexample, the vocabulary service 122 may ascertain the reader's top tenterms most often looked up or highlighted.

Once terms are collected, the vocabulary service 122 can generatevocabulary questions designed to test a reader's comprehension of theterms. In particular, the vocabulary service 122 can create questionsthat take into account the context from the eBook from which the termwas extracted. In this manner, the data structure 126 may furtherinclude excerpts of the eBooks that include surrounding text portionsencompassing the terms of interest.

FIG. 2 illustrates metadata 200 which may be used by the architecture ofFIG. 1. In addition to the contents of the eBooks themselves, data aboutthe works, or metadata, may be used to determine context and relevantcorrespondences between terms, eBooks, users, and so forth.

Book metadata 202 may be accessed by the vocabulary service 122 foranalysis. The book metadata may include a publication year 202(1)indicating when the eBook, either in physical printed or electronicform, or was otherwise published. An author or authors 202(2) may bestored, as well as categories such as whether the book is fiction ornon-fiction 202(3), genre 202(4), and so forth. Other, comparable books202(5) such as other books in a series, books related by topic, and soforth, may be stored. Other data 202(B) such as topic, sales volume,user ratings, number of revisions or editions, and so forth may also bemaintained and used.

Metadata may also be stored within the vocabulary service 122 withregards to a particular target term, such as a single word or phrase.Term metadata 204 may include a date of origin for each definition204(1), other terms which the target term or phrase are commonlyproximate to 204(2), or frequency of use 204(3) for each definition.Dialect information 204(4), type of part of speech 204(5), may also beavailable. The term metadata 204 may include other data 204(W) such asrate of change of use, etymological information, and so forth.

User metadata 206 may also be used by the vocabulary service 122. Theuser metadata 206 may include a geographic location of the user 206(1),a date of birth 206(2), educational level 206(3), occupation 206(4),friends/associates or social network 206(5), other comparable users206(6), books previously read by the user 206(7), preferredlanguages/dialects 206(8) known to the user, and other information206(U).

FIG. 3 illustrates an example architecture 300 in which vocabularyquestions are generated to test the readers' comprehension of the termsof interest. In architecture 300, the vocabulary questions are generatedby the vocabulary service 122, although in some implementations, thequestions may be created locally by the electronic devices 106. Thevocabulary service 122 receives the terms from the electronic devices106 over the network 120 and utilizes internal and external resources togenerate the questions.

In one approach, the servers 124(1)-(S) implement a vocabulary questionengine 302 to craft the vocabulary questions. The vocabulary questionengine 302 evaluates the terms in the context of the eBooks within whichthe readers requested a definition or otherwise indicated an interest.The eBooks are identified in the data structure 126 by their associationwith the terms. The vocabulary question engine 302 extracts the passagefrom the eBook that contains the term of interest and parses the passageto ascertain the context. The vocabulary question engine 302 can thenautomatically generate a question based on the parsed passage. In oneimplementation, the engine 302 may simply extract the passage and askthe reader to fill in the blank. In another implementation, thevocabulary question may be formatted as multiple-choice, where thequestion restates the passage with a blank for the vocabulary term beingtested, and provides multiple terms as possible answers. One of theanswers is the term that is being tested. The remaining possibilitiesare confounders that are intended to provide plausibly legitimate, butincorrect, alternatives.

For instance, suppose the engine 302 receives the term “fair”, which wasindicated as being of interest to the reader 102(1) when reading theeBook passage from Romeo and Juliet in FIG. 1. Based on this input, thevocabulary service engine 302 might automatically craft the vocabularyquestion by extracting the passage from the associated eBook thatcontains the term to be tested and providing a set of confounderstogether with the correct answer, as follows:

-   -   Arise, ______ sun, and kill the envious moon . . . .        -   A. bright        -   B. fair        -   C. round        -   D. yellow

Picking confounders (i.e., the incorrect responses) is non-trivial.Generally, the vocabulary question engine 302 will choose other termswith the same part of speech (e.g., if the term to be tested is anadjective, the confounder term is also an adjective). The engine 302 mayfurther select synonyms and antonyms of the term being tested by thevocabulary question. Moreover, the genre or context of the work may beconsidered when choosing confounders. For instance, when the term “fair”is extracted from the work Romeo and Juliet, confounders that pertain tovisual attributes (i.e., “fair” means flawless beauty in this context)may be selected as being more appropriate for this work and more likelyto build the reader's vocabulary based on this period work. However, theterm “fair” from another book, such as the passage 112 may involvedifferent confounders pertaining to the idea of a gathering rather thanvisual attributes.

In another approach, the vocabulary service 122 may submit the terms andassociated eBook passages to a mechanical solutions network 304 thatutilizes human resources to draft the vocabulary questions or selectappropriate definitions for a term in a particular context or passage.As shown here, the mechanical solutions network 304 provides a computersystem that sources projects with various researchers 306(1), 306(2), .. . , 306(J). The researchers 306(1)-(J) receive the terms and eBookpassages, and formulate vocabulary questions that test the reader'sunderstanding of the terms, either alone, or in reference to theparticular context in the eBook passage. For instance, suppose aresearcher 306(1) received the term “fair” and a reference to the work,Renaissance Day. After reviewing this passage, the researcher may pen avocabulary question, as follows:

-   -   The term “fair” is used as a noun in which of the following        phrases?        -   A. The ale was a fair price.        -   B. It was perhaps the most exquisite fair I have ever seen.        -   C. She was the fairest of them all.        -   D. None of the above            Examples of the mechanical solutions network 304 include            Mechanical Turk™ system provided by Amazon, Inc. of Seattle,            Wash., and mobile texting services, such as the service            provided by ChaCha Search Inc. of Indianapolis, Ind.

The vocabulary service 122 may further leverage other vocabulary supportresources 308 to assist in creating vocabulary questions. Suchvocabulary support resources 308 may include sites or services thatprovide such vocabulary support as dictionary services, thesaurusservices, spelling services, trivia services, and so forth. In this way,the vocabulary question engine 302 may craft at least part of thequestion using information retrieved from such third party resources308. For example, the vocabulary engine 302 may receive a term ofinterest and utilize a third party resource 308, such as a thesaurusservice, to develop confounders to use as false answers in thevocabulary question.

Once generated, the vocabulary service 122 serves the vocabularyquestions back over the network 120 to the respective electronic devices106 that provided the terms originally. The vocabulary service 122 usesthe unique ID associated with the terms in the data structure 126 todetermine where to return the vocabulary questions. In FIG. 3, thereader 102(1) had requested a dictionary definition of the term “fair.”Based on that request, the vocabulary service 122 crafts the questionnoted above pertaining to the term “fair” and serves that question backto the electronic device 106(1) being used by the reader 102(1). Theelectronic device 106(1) renders the vocabulary question 310 on thedisplay 110 to test and build the reader's vocabulary. Similarly, thequestion pertaining to the term “fair” is served back to the electronicdevice 106(D), which renders the vocabulary question 312 on the display114 to test the reader 102(N).

FIG. 4 shows the electronic book reader device 106(1) with a questionuser interface (UI) 400 that presents the vocabulary question 310 to areader. In this illustration, the question UI 400 includes a questionpresentation panel 402 that contains the question 310 in written form.The panel 402 may be overlaid on the eBook to which the vocabularyquestion pertains. As shown here, the panel 402 is overlaid on a coverimage of the eBook Romeo and Juliet, from which the vocabulary terms areextracted. The reader may enter a response using the keyboard 404 bytyping in one of the multiple-choices of A, B, C, or D. Alternatively,the reader may enter an answer by moving a pointer 406 via a navigationmechanism 408 (e.g., thumbwheel, joystick, touchpad, etc.) to select oneof the multiple-choice answers. Upon entry of an answer, the userresponse is processed either internally by the eBook reader device106(1) or by the vocabulary service 122.

FIG. 5 shows the electronic book reader device 106(1) with a vocabularyresults UI 500 that is presented in response to the reader entering ananswer in the question UI 400 of FIG. 4. The results UI 500 has a panel502 to present the results of the reader's answer. The panel 502 mayoptionally restate the vocabulary question 504 and then reveals thereader's answer 506 along with an indication 508 as to whether thereader was correct. In this example, the reader answered “B. Fair” 506,which is noted as being correct 508. The results panel 502 may furthershow the reader's testing statistics 510.

In one implementation, the vocabulary questions may be submitted to manyreaders in the reader population 104. The vocabulary service 122 maythen track the answers from the members and compute statistics. Forinstance, suppose many of the readers 102(1)-(N) were given the samequestion posed in FIG. 4:

-   -   Arise, ______ sun, and kill the envious moon . . . .        -   A. bright        -   B. fair        -   C. round        -   D. yellow            After some period of time, the vocabulary service 122 finds            that 78% of the community answered correctly with choice “B.            fair”, while the choice “A. bright” placed second with 17%            of the responses, the choice “D. yellow” finished third with            3%, and the choice “C. round” finished fourth with 2%. These            group statistics may also be presented in the panel 502 so            that the reader can compare her results with those of other            readers. Moreover, the vocabulary service 122 may operate            vocabulary games or other contests that allow readers to            compete with one another.

In the results UI 500, the answer 506 may include the percentage ofreaders who responded with that same answer. Here, 78% of the readerpopulation answered correctly with choice “B. Fair”. The panel 502 mayfurther include a listing 512 of the other choices, along withpercentages of responses from the reader population. A “See Excerpt”control 514 may be selected by the reader to show the full excerpt fromwhich the vocabulary term was extracted. The reader may use the keyboard404, navigation mechanism 308, or other control buttons to select thecontrol 514. Moreover, the display may include a touch responsive screenthat allows the user to select the control 414 through touch orproximity to the control 514.

FIG. 6 shows the vocabulary results UI 500 after the reader actuates the“See Excerpt” control 514. An excerpt box 602 is invoked and positionedover the panel 502 to show the full context of the eBook from which theterm being tested was extracted. The term being tested may also beemphasized in some manner, such as by underlining, bolding,highlighting, or some other focus technique.

With reference again to FIG. 3, the vocabulary service 122 may furthergive the reader 102(1) an opportunity to rate the vocabulary question.In addition, the questions may be rated by other readers from the readerpopulation 104 who are interested in learning the same term. In thismanner, the community functions as feedback to validate theeffectiveness of the questions, particularly those that wereautomatically generated. The rating process may take any number offorms, such as from simply asking “Was this vocabulary questionhelpful?” to ranking various questions that attempt to test the reader'svocabulary of a given term.

FIG. 7 shows the electronic book reader device 106(1) with a rating UI700 that may be presented to the reader following the question andanswer session. The rating UI 700 has a panel 702 configured to seek thereader's feedback on the question. In this illustration, the panel 702asks the simple question “Was this vocabulary question helpful?” andprovides two response controls: a “yes” control 704 and a “no” control706. The reader may elect one of the controls 704 and 706 and theresponse is returned to the vocabulary service for processing. Thecontrols may be selected using the keyboard 404, the navigationmechanism 408, other physical control buttons, or soft keys via atouch-responsive screen.

With reference again to FIG. 3, in another implementation, the service122 may post the vocabulary questions for discussion and debate in anonline discussion format. Members of the reader population may reviewthe vocabulary questions and post comments. Other members may thencomment on others' postings. The members may discuss whether thequestions are appropriate in terms of difficulty or helping build thereader's vocabulary. In an education setting, the community may helpsift through questions that would be appropriate for different readinglevels, such as grammar school, middle school, or high school.

The vocabulary service 122 (or the device 106 itself) may also trackresponse time that gauges a reader's effectiveness in answering thequestions. The response time may be measured as the time duration frompresentation of the vocabulary question until the reader enters ananswer. This feature may be used, for instance, when administering anexam or when comparing users in a reputation system. These response timemetrics may then be tracked and provided as feedback to the user or maycontribute to score in a game or be used to affect the user's rating ina reputation system.

In the above discussion, the vocabulary questions and answers have beendescribed as being textual in nature, where questions and possibleanswers are written and displayed. In some implementations, however, thedevices for text-based digital work may include a text-to-speech featurethat converts the text into an audible form, whereby the deviceeffectively reads the text to the user. In such situations, thequestions and answers may be presented in an audible form as well.

Illustrative Vocabulary Information UIs

FIGS. 8-10 illustrate an example architecture 800 in which thevocabulary service 122 provides more extensive information pertaining tothe vocabulary terms that the reader has indicated an interest inlearning, as well as terms of interest to the entire population ofreaders. As shown in FIG. 8, the vocabulary service 122 includes avocabulary information server 802 that executes on the servers124(1)-(S). The vocabulary information server 802 provides informationabout the vocabulary terms and packages that information in variousviews or page renderings tailored for various perspectives. Forinstance, one view of the information may be from a reader'sperspective, showing the terms that the reader identified as being ofinterest (e.g., looked up a definition, annotated the term or phrase,highlighted the term or phrase, etc.) and information about those terms.Another view may be from the term perspective, showing commondefinitions, sample vocabulary questions, the eBooks from which theterms are extracted, and so forth. Another view may be from the eBookperspective, showing the terms most commonly noted of interest by thereader population. Examples of these perspectives are provided in thisFIG. 8 and in later FIGS. 9-10. Many other perspectives are possible.

A representative reader 102(1) from the population 104 (FIG. 1) mayaccess the vocabulary information via a computing device 804 over thenetwork 120. The computer 804 may run a browser that requests andrenders web pages from the vocabulary information server 802 to form avocabulary information user interface (UI) 806. Thus, in the examplesillustrated herein, the screen renderings are illustrated as web pagesrendered within a browser. However, this is merely one possibleimplementation, and other technologies may be employed to facilitate avocabulary information UI. Further, in this example, the computingdevice 804 is a desktop computer equipped with a browser. However, otherdevices may be employed by the reader 102(1), such as a laptop computer,PDA, communications device, notepad computer, or essentially any deviceequipped with a browser or other rendering application to receive andrender web pages or other inactive interfaces provided by the vocabularyservice 122.

The reader 102(1) is also illustrated with her electronic device 106(1),which is depicted as rendering the vocabulary question UI 400 from FIG.4. In some implementations, the electronic device 106(1) may be equippedwith a browser or other rendering application to receive and render theinformation pages served by the vocabulary information server 802. Inthis manner, the reader 102(1) may use a single electronic device 106(1)for multiple purposes, including reading eBooks, receiving and answeringvocabulary questions, and accessing the vocabulary information server702 to learn more information pertaining to the vocabulary terms.

The vocabulary information UI 806 presents vocabulary information fromvarious perspectives, and allows the reader to navigate theseperspectives to gain a deeper understanding of the vocabulary terms.FIGS. 8-10 show a series of screen renderings of the vocabularyinformation UI 806. The screen renderings are illustrated as web pagesrendered within a browser. However, as noted above, this is merely onepossible implementation and other technologies may be employed tofacilitate electronic user entry of questions.

In FIG. 8, a first screen rendering 808 of the vocabulary information UI806 is a reader's page that provides the reader's perspective of thevocabulary information. That is, the reader's page 808 containsinformation pertaining to a particular reader, such as the reader102(1), in regards to the vocabulary terms that the reader haspreviously noted as being of interest. The reader's page 808 has anidentity tile 810 to personalize the page as being associated with thereader 102(1). The identity tile 810 may include an image of the reader(if one exits) and any pertinent information, such as his residence orother address. The reader may provide this personal data as part of aregistration process and that information is associated with theparticular reader at the vocabulary service 122.

The reader's page 808 includes a primary area 812 to the left of theidentity tile 810. The primary area 782 is separated into threedemarcated zones. A first or top zone 814 allows the reader 102(1) toindicate which eBooks she is currently reading, such as Good to Great byJim Collins as represented by a thumbnail 816 and Romeo and Juliet byWilliam Shakespeare as represented by a thumbnail 818. A second ormiddle zone 820 may be used to present vocabulary questions to thereader based on terms the reader has shown an interest. Here, avocabulary question from the eBook Romeo and Juliet is shown. Thequestions may be randomly selected for the reader based on termscollected over time or be specific to the eBooks that the reader listsin the first zone 714. Moreover, the questions may be repeated until thereader shows an improved accuracy.

A third or lower zone 822 identifies vocabulary terms relevant to thereader. In this example, the most recent vocabulary terms 824 taken fromthe current eBooks are listed, including the terms “yonder” and “fair”from the eBook Romeo and Juliet and the term “hedgehog” from the eBookGood to Great. Each term in the list 824 is a link to another view ofthe vocabulary information from the term's perspective. Severalvocabulary-building options may also be provided for selection by thereader to seek more information about the terms. For example, a “viewquestion” control 826 allows the reader to see one or more questionspertaining to that term. The questions may be presented in the middlezone 814. A “view excerpt” control 828 allows the reader to see theexcerpt from the eBook in which the reader first noted the term (e.g.,sought a definition, highlighted it, etc.). An “eBook” control 830 mayalso be provided to navigate to the eBook from which the vocabulary termwas extracted. These are just example controls, and others are possible,such as a link to a dictionary definition, or a link to a thesaurus.

It is noted that the primary area 812 may be segmented into more or lessthan three zones. Further, the zones may be of any shape. Moreover, thecontent of each zone is merely representative. In other implementations,the zones may include any number of things, such as eBooks that thereader intends to purchase or read next, discussion boards for thereader population, and so forth.

Also shown on the reader page 808 is a recommendation region 832 beneaththe identity tile 810. In the recommendation region 832, recommendationsmay be made to the reader to learn more about a particular term. Forinstance, suppose the reader has struggled with the term “yonder”, sinceit is listed as the number one vocabulary term in zone 822. Therecommendation region 832 may provide suggestions about where to learnmore about the term “yonder,” or other books in which the term appearsfrequently.

FIG. 9 shows a second screen rendering 902 of the vocabulary informationUI 806 that is presented in response to the reader activating the termcontrol 824 for the term “yonder” (FIG. 8). The screen rendering 902 isa term page that provides a view of the vocabulary information from theterm's perspective. In this example, the term page 902 is for theparticular vocabulary term “yonder” as noted by the heading at the topof the page. The term page 902 has a details area 804 that containsdetails about the term. Here, the details area 804 includes a definitionof the term “yonder”, along with synonyms and antonyms. Each entry inthe synonyms and antonyms may also be formatted as an actuatable linkthat navigates to other term pages for these specific terms. The termpage 802 further has a question entry area 806 which allows the readerto enter a question that can be used to test and build the reader'svocabulary. The reader may enter the question into the box in area 806and the question is saved in association with the reader's identity andthe term of interest (i.e., “yonder” in this example). In this way, thereader can tailor questions suitable for his or her learning style toimprove comprehension of terms.

The term page 902 also has relevance area 908 that explores current usesof the term of interest as gathered from various sources. For instance,the relevance area 908 may show a ranking 910 of the term “yonder”within a social networking service or a microblog service (e.g.,“Twitter.com”, which is owned and operated by Twitter, Inc. of SanFrancisco, Calif.). The area 908 may also show actual microblog entries912 that contain the term “yonder”, such as the entries from bloggers Aand B. In this example, Blogger A writes, “I love Shakespeare. Don't seeterms like yonder much today.” Blogger B subsequently posts “Do you readNathanial Hawthorne . . . . Come away, or yonder old Black Man willcatch you!” This is an excerpt from Hawthorne's work, The ScarletLetter, in which the full excerpt is as follows:

-   -   Come away, mother! Come away, or yonder old Black Man will catch        you! He hath got hold of the minister already. Come away,        mother, or he will catch you! But he cannot catch little Pearl!

Alternatively, the relevance area 908 may include how the term is beingused as a tag to identify other books or items, or instances where theterm is being used in reviews. There are many diverse sources ofinformation from which to collect and aggregate information about theterm, which in turn may assist the user in learning more about the termof interest.

The term page 902 further includes a reference area 914 arranged as avertical column that indicates sources from which readers have noted aninterest in the term. Two eBooks are illustrated for example purposes todemonstrate possible sources for the term “yonder,” although there maybe many other references. The first source presented in the referencearea 914 is the eBook Romeo and Juliet, as indicated by the thumbnailimage 916, along with an excerpt 818 from the eBook that contains theterm “yonder.” The second source shown in the reference area 914 is theeBook History of the US Air Force, as indicated by the thumbnail image920, along with an excerpt 922 from the eBook that contains the term“yonder.” The images 916 and 920 are also fully actuatable andassociated with respective eBook pages, such that upon selection by thereader, the browser navigates to a page for that particular eBook.

FIG. 10 shows a third screen rendering 1002 of the vocabularyinformation UI 806 that is presented in response to the readeractivating the eBook image 916 for the eBook Romeo and Juliet (FIG. 9).The third screen rendering 1002 is an eBook page that provides a view ofthe vocabulary information from the eBook's perspective. The eBook page1002 includes a header region 1004 to identify the eBook. A thumbnailimage 1006 of the eBook cover may be included in this region 1004. TheeBook page 1002 further includes a details area 1008 with details aboutthe book, such as a description, reviews, and so forth. The details area1008 may further include a “Buy Now” control to facilitate purchase ofthe book, as well as a “Comment Now” control to enter comments about thebook.

Also provided on the eBook page 1002 is a vocabulary terms area 1010that contains one or more listings of vocabulary terms from the eBookthat were identified by readers over time. The listing may be organizedin many different ways. In this illustration, the terms most often notedto be of interest from the eBook Romeo and Juliet are shown in twoseparate and numerated lists: a first list 1012 providing the terms ofinterest to the community of readers and a second list 1014 providingthe terms of interest to the particular reader. In the first list 1012,a count of the number of readers who looked up the term or otherwiseindicated an interest is indicated in parentheses following the term(e.g., 1039 people cited an interest in the term “envious”). Thisarrangement may be a default, or be invoked through selection of the“most interest” control 1016. Notice that each term in the lists 1012and 1014 are links that take the user to the corresponding term page,such as the term page 902 for “yonder” shown in FIG. 9.

The vocabulary terms may be ordered in other arrangements throughselection of other various controls. For example, an alphabeticalcontrol 1018 arranges the vocabulary terms from the particular eBook inalphabetical order and a reading level control 1020 ranks the terms ofinterest according to a reading metric. Other arrangements may beaccording to most looked up terms, most quoted terms, most highlightedterms, most annotated terms, most recent terms indicated as being ofinterest, and so forth. Moreover, the terms may be arranged according toexternal indicia, such as the terms that most frequently appear in acrossterm puzzle of a particular source (e.g., New York Times crosstermpuzzles), or the terms that are rising fast in rankings on a socialnetwork or blogging service.

The vocabulary terms area 1010 may further include a “test me” control1022 that provides a series of vocabulary questions to test the reader'scomprehension of the terms in the particular eBook.

FIG. 11 shows a fourth screen rendering 1102 of the vocabularyinformation UI 806 that presents a general vocabulary page which is notspecific to a term, or reader, or eBook. Rather, the vocabulary page1102 simply pertains to vocabulary. The vocabulary page 1102 isidentified as such by the heading 1104, such as “vocabulary central.”The vocabulary page 1102 presents information from a vocabulary buildingperspective. The page may be structured in any number of ways, with manyvarious graphical layouts. In this example, the page 1102 includes listsof popular vocabulary terms, such as a list 1106 of the five most lookedup terms or a list 1108 of the five most recently looked up terms. Alist 1110 of the five eBooks with the most looked up terms may also beprovided on the page 1102.

The vocabulary page 1102 may further include a question region 1112 topresent random vocabulary questions that have been crafted over time. Adifferent vocabulary question may be presented with each reloading ofthe page.

The vocabulary page 1102 may also have a new eBooks area 1114 thatpresents the latest or most popular new eBooks. The eBooks may beidentified through representative thumbnail images, such as images 1116and 1118, along with a corresponding list of terms most often referencedin the new eBooks.

Enhanced Definitions and Samples

FIG. 12 shows an enhanced definition UI 1200 that is presented inresponse to a user query about the definition of a particular term. Ascreen rendering 1202 depicts a term page that provides a view of theterm and detailed information about the term and various meaningsassociated with it. In this example, the term page 1202 is for thevocabulary term “fair” as noted by the heading at the top of the page.The term page 1202 has a details area 1204 that contains details aboutthe term. Here, the details area 1204 includes definitions of the term“fair.” In some implementations, other information such as synonyms andantonyms may also be presented. The synonyms and antonyms entries mayalso be formatted as actuatable links that navigate to other term pagesfor the specific terms. The term page 1202 further has a question entryarea 1206 which allows the reader to enter a question. The reader mayenter the question into the box in area 1206 and the question is savedin association with the reader's identity and the term of interest(i.e., “fair” in this example). In this way, the reader can tailorquestions suitable for his or her learning style to improvecomprehension of terms.

The term page 1202 also has relevance area 1208 that explores currentuses of the term of interest as gathered from various sources. Forinstance, the relevance area 1208 may show a ranking 1210 of the term“fair” within a social networking service or a microblog service (e.g.,“Twitter.com”, which is owned and operated by Twitter, Inc. of SanFrancisco, Calif.). The area 1208 may also show actual microblog entries1212 that contain the term “fair,” such as the entries from bloggers Aand B. In this example, Blogger A writes, “I love Shakespeare. Don't seefair used like this much anymore.” Blogger B subsequently posts “Fairthe well!”

Alternatively, the relevance area 1208 may include how the term is beingused as a tag to identify other books or items, or instances where theterm is being used in reviews. There are many diverse sources ofinformation from which to collect and aggregate information about theterm, which in turn may assist the user in learning more about the termof interest.

The term page 1202 further includes a reference area 1214 arranged as avertical column that indicates sources from which readers have noted aninterest in the term. Two eBooks are illustrated for example purposes todemonstrate possible sources for the term “fair,” although there may bemany other references. The first source presented in the reference area1214 is the eBook Romeo and Juliet, as indicated by the thumbnail image1216, along with an excerpt 1218 from the eBook that contains the term“fair.” The second source shown in the reference area 1214 is the eBookJohn Lyly's England, as indicated by the thumbnail image 1220, alongwith an excerpt 1222 from the eBook that contains the term “fair.” Theimages 1216 and 1220 are also fully actuatable and associated withrespective eBook pages, such that upon selection by the reader, thebrowser navigates to a page for that particular eBook.

A user actuable control may also be presented to provide moreinformation about the use of the term 1224. Information about the use ofthe term, including statistical data is described next with regards toFIG. 13.

FIG. 13 illustrates an architecture 1300 for analyzing the changes of aterm over time based upon data in a corpus of content items by thevocabulary information server 802. The screen rendering 1302 is a trendpage that provides a view of the term and the changes in meaning overtime as determined from analysis of content items within the corpus. Inthis example, the trend page 1302 is for the term “fair” as noted by theheading at the top of the page. The trend page 1302 has a details area1304 that contains a table describing changes in the definition of theterm “fair” by century.

As shown in this table, the predominate use of the term “fair” hasshifted from being predominately “flawless” in the 16^(th) century topredominately “impartial” in the 21^(st) century. The trend page 1302thus provides an opportunity to see how the use of a term has evolvedover time, and improve comprehension by the user.

In some implementations, this information may also allow a user toanticipate a new usage of a term. Such information may be useful in theformation of marketing programs to either target an advertising messagewith terms having a new and upcoming meaning, or to avoid an inadvertentassociation. For example, as shown here, given that the term “fair” hasbegun to be used to mean “beyond good” in the 21^(st) century, anadvertising campaign may be crafted to use this new meaning.

A filter option area 1306 arranged as a vertical column presents severaluser actuable controls which may be used to alter the presentation ofthe data on the trend page 1302. A change filter(s) command 1306(1)allows the user to alter existing filters which are applied. A commandto set a time period 1306(2) provides a mechanism for the user to changethe time period of the trend. For example, the user may wish to set thetime period to the past 12 months, rather than the past six centuries,as shown here.

The filter options 1306 may also include a filter by topic 1306(3). Forexample, a filter may be applied showing the use of the trends for aterm within the topic of “jurisprudence.” A filter by genre 1306(4)allows for selection of trend information by genre such as mystery,science-fiction, and so forth. Additionally, the user may filter byother factors 1306(F). These factors may include metadata such asdescribed above with respect to FIG. 2. For example, the user may chooseto filter to see trend information from books read by readers betweenthe ages of 18 and 25.

FIG. 14 illustrates an architecture 1400 for providing examples of aselected vocabulary term, the examples having been drawn from the corpusof content items by the vocabulary information server 802.

The screen rendering 1402 is an example page that provides a view of theterm, a selected definition, and examples of the term in use from actualpassages. In this example, the example page 1402 is for the term “fair”as noted by the heading at the top of the page. The example page 1402has a details area 1404 that contains examples of the term which havebeen excerpted from books which are relevant to the user.

As shown here, the examples include three books, showing passages fromsports, history, and ethics. Thus, the user is able in example page 1402to see the term in action for a selected definition. Should a userbecome interested in a particular book for which an excerpt has beenprovided, the user may select a user actuable control and explore thatbook in more detail, or take other actions such as purchasing the workand so forth.

A filter option area 1306 arranged as a vertical column presents severaluser actuable controls which may be used to alter the presentation ofthe data on the example page 1402. As described above, these controlsmay include the functions such as the change filter(s) 1306(1), the seta time period 1306(2), the filter by topic 1306(3), the filter by genre1306(4), or other factors 1306(F). These factors may include metadatasuch as described above with respect to FIG. 2. For example, the usermay choose to see examples of the term “fair” as used in non-fictionbooks identified with the topic “jurisprudence” and published in thepast five years.

FIG. 15 illustrates an architecture 1500 for enhancing a search by usinga determined meaning of the search term as provided by the vocabularyinformation server 802. The screen rendering 1502 is a search page inwhich the user has initiated a search for the phrase “fair policy” andreceived search results in a details area 1504.

As shown here, the search results in the details area 1504 include twobooks and one video documentary. Based upon the search term of “fair” incombination with the term “policy,” the vocabulary information server802 determined the intended meaning of the term “fair” in this contextas being “impartial.” Thus, the search results show books using the termwith the same meaning.

The search page 1502 further includes a reference area 1506 arranged asa vertical column that indicates books which use the second most likelydefinition of the term “fair,” that of “a gathering of buyers andsellers.” Two eBooks are illustrated for example purposes to demonstratepossible books which use the term “fair” in this alternative definition,although there may be many other references. The first source presentedin the reference area 1506 is the eBook How to Barter, as indicated bythe thumbnail image 1508, along with an excerpt 1510 from the eBook thatcontains the term “fair” with this alternate meaning and the term“policy.” The second source shown in the reference area 1506 is theeBook Laws for Farmer's Markets, as indicated by the thumbnail image1510, along with an excerpt 1512 from the eBook that contains the term“fair” also with the alternate definition and the term “policy.” Theimages 1508 and 1510 are also fully actuatable and associated withrespective eBook pages, such that upon selection by the reader, thebrowser navigates to a page for that particular eBook.

Server-Based Vocabulary Game

FIG. 16 illustrates an example architecture 1600 in which the vocabularyservice 122 further supports a network-based vocabulary game 1602. Thevocabulary game 1602 is shown as being part of the service offerings ofthe vocabulary information server 802, although the game 1602 may beoperated entirely separate from the server 802. Moreover, the vocabularygame 1602 may be configured to supply the game to third party services1604. For instance, suppose that the third party service 1604 providesvocabulary support services, such as dictionary or thesaurus resources.A game player 1606 may have an account with the third party service1604, which permits access to more resources. Among these additionalresources may be the vocabulary game that permits the player to buildher vocabulary through interactive games.

The player 1606 accesses the network-based vocabulary game 1602 via acomputer 1608. In some implementations, the game is delivered over theInternet according to web-based protocols, such as HTTP. The computer1608 executes a browser (or other rendering application) to request andreceive pages, which are rendered to provide a game UI 1610. In otherimplementations, the game maybe delivered over other types of networksusing the same or different protocols.

The game UI 1610 includes a title 1612 (e.g., “Vocabulary Game Place”)and question region 1614 to present vocabulary questions. The types ofquestions may be based on a set of user-selected criteria shown invertical region 1616. Example criteria might include, for example, termsof a particular reading level as indicated by the “reading level”control 1618, terms of particular topics as indicated by the “topical”control 1620, terms from a specific book as indicated by the “bookspecific” control 1622, and terms of a particular genre or category asindicated by the “category” control 1624.

Once the gamer player selects the types of questions, vocabularyquestions are presented in the region 1614. Suppose, for example, theuser selected the “book specific” criteria 1622 and particularly booksby Shakespeare. A series of vocabulary questions involving termsextracted from various Shakespeare works would be serially depicted inthe region 1614.

The first question is presented when the player 1606 starts the game. Atimer 1626 is initiated to provide a countdown of the time remaining. Asthe player responds to each question, a next question is presented and aquestion count 1628 is maintained to let the player understand how manyquestions are left in the allotted time 1626. A tally 1630 of correctand incorrect answers is also updated on the fly as the player 1606plays the game.

The layout of the game UI 1610 is but one example. Many other layoutsand designs are possible.

FIG. 17 illustrates a further example of the architecture 1600 in whichthe vocabulary service 122 further supports a network-based vocabularygame 1702 to generate confounders for vocabulary questions.

The player 1606 accesses the network-based vocabulary game 1702 via thecomputer 1608. In some implementations, the game is delivered over theInternet according to web-based protocols, such as HTTP. The computer1608 executes a browser (or other rendering application) to request andreceive pages, which are rendered to provide a game UI 1610. In otherimplementations, the game maybe delivered over other types of networksusing the same or different protocols.

The game UI 1610 includes a title 1702 (e.g., “Confound Me Game”) andthe current definitions of the term. In this example, the term is “fair”and the definition for which a confounder is sought is “flawless.” Alsopresented is a question region 1706 to present information aboutconfounder questions. A user input area 1708 allows entry of theproposed confounder question. For example, here the user entered “Herchildren always found her fair.” This is a confounder because it theappropriate definition of “fair” in this scenario is ambiguous.

The game presented here challenges the user to generate confounderswhich will mislead a reader. The first term and definition is presentedwhen the player 1606 starts the game. A timer 1710 is initiated toprovide a countdown of the time remaining. As the player responds toeach question, a next term and definition is presented and a confoundercount 1712 is maintained to let the player understand how manyconfounders are left to be completed in the allotted time 1710.Statistics 1714 about the response of other readers to the confoundersare also presented.

A control area 1716 arranged as a vertical column may also be presentedin the UI 1610. These controls allow the user to make changes to thegame, seek help, and so forth. A user actuable control to select a newdefinition 1718 is present. With this control, the user may pick anotherdefinition to work on for the current work, for example, changing to thedefinition of “impartial.” A new term 1720 control allows selection ofanother term. This term may be selected from those used to generate theuser's vocabulary questions, or from other users.

Also, the user may have difficulty in visualizing confounders for thisterm. A see other confounders 1722 control is present to update the UI1610 to show some other confounders which have been previously entered.

Other variations of the game are possible. In one such variation theuser may generate an incorrect but plausible alternative definition fora term. For example, the user might define generate a false definitionof fair as “a free outdoor show.” Once generated, vocabulary questionsmay incorporate the confounders.

Illustrative System

FIG. 18 shows selected modules in a representative computer system 1800that may be used to collect terms of interest to readers, and generatevocabulary questions for those terms to test the readers' understanding.The system 1800 includes the servers 124(1)-(S) of the vocabularyservice 122 and the electronic devices, as represented by an electronicdevice 106, from FIG. 1. The servers 124(1)-(S) collectively provideprocessing capabilities 1202 and memory 1804. The memory 1804 mayinclude volatile and nonvolatile memory, removable and non-removablecomputer-readable storage media implemented in any type or technologyfor storage of information, such as computer-readable instructions, datastructures, program modules, or other data. Such memory includes, but isnot limited to, RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disks (DVD) or other opticalstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, RAID storage systems, or any othermedium which can be used to store the desired information and which canbe accessed by a computing device.

Stored in the memory 1804 is a digital work collection module 1806,which defines multiple databases. In this example, the digital workcollection module 1806 includes a customer database 1808, an eBookdatabase 1810, and a vocabulary term database 1812. The customerdatabase 1808 contains information about readers 102(1)-(N) in thereader population 104. The eBook database 1810 maintains a collection ofeBooks that may be accessed and delivered to the readers' electronicdevices 106. The vocabulary database 1812 stores the vocabulary termscaptured by the readers while reading eBooks on the devices 106. Theterms are stored in association with the eBook from which the term wasextracted, along with an identity of the reader and/or device, in a datastructure 126 maintained within the vocabulary database 1812.

The vocabulary question engine 302 is shown embodied as a softwaremodule that is stored in the memory 1204 and executable by the processor1802. The vocabulary engine 302 receives terms from the electronicdevice 106 that the reader expressed, implicitly or explicitly, aninterest in learning. For instance, the reader may have requested adefinition of the term, or highlighted the term, or annotated the work,or engaged in some other activity indicative of interest in the term.When the term is received from the device 106, it is stored in thevocabulary term database 1812 in association with the eBook from whichthe reader noted the term.

The vocabulary question engine 302 generates questions for the terms inthe vocabulary term database 1812. The questions may be in manydifferent formats, such as multiple-choice, fill-in-the-blank,true/false, or open ended. In some cases, the vocabulary question engine302 may use portions of the text in the eBook to state a question. Inthis situation, the vocabulary question engine 302 identifies theassociated eBook from the vocabulary term database 1812, and accessesthe eBook from the eBook database 1810. The vocabulary question engine302 restates the excerpt in the form of a question to test the reader'scomprehension of the term. The vocabulary engine may then serve thevocabulary question back to the electronic device 106 from which theterm was originally received. The identity of the electronic device isdiscovered in the data structure 126 maintained in the database 1812.

The vocabulary question engine 302 may include a confounder module 1814which produces a set of confounders for multiple-choice questions. Forinstance, the confounder module 1814 may choose synonyms and antonyms ofthe term being tested. The confounder module 1814 may further chooseotherwise correct dictionary definitions of the term, but which areincorrect in the context of the eBook from which the term was selected.For instance, the term “blue” may mean a color, but when used in theeBook the term meant an emotional state.

A timer 1816 is provided to time the reader's response time. The timer1816 measures the time period between a time when a reader is presentedwith vocabulary question and a time when the reader submits a response.The timer may further time an overall testing duration when a reader ispresented with multiple questions.

The vocabulary question engine 302 may also implement a statisticsmodule 1818 to compute various measurements of relating to which termsare selected, accuracy of the reader's responses, and so forth. Forinstance, the statistics module 1818 may ascertain which terms arelooked up most frequently, which terms are highlighted or annotated mostoften, which terms the reader correctly or incorrectly answerscorresponding questions, and so forth. The statistics module 1818 mayfurther compute these statistics relative to books, such as the mostfrequently cited terms in the eBook, or top ten books in which aparticular term is looked up. Furthermore, similar statistics can bemeasured relative to the reader or eBook device. For example, thestatistics module 1818 may compute the reader's top ten terms most oftenlooked up or highlighted.

The vocabulary information server 802 is shown embodied as a softwaremodule that is stored in the memory 1804 and executable by the processor1202. The server 802 provides vocabulary information to facilitate adeeper understanding of the terms, thereby enriching the reader'slearning experience. In one implementation, the server 802 serves theinformation in pages that, when rendered on a browser or otherapplication, provide an interactive UI with data and references aboutvarious vocabulary terms. One example UI 806 is shown in FIGS. 8-11. Theserver 802 may also include a rights holder reporting module 1820, whichprovides aggregated results to rights holders, such as authors,publishers, agents, heirs, estates, and so forth. The reporting module1820 may provide such information as which terms are most often lookedup by the readers in the book. This may provide some insight as to thereader's ability to understand the work.

The servers 124(1)-(S) communicate with one or more devices 106(1)-(D),as represented as device 106 in FIG. 18. The device 106 has a processor1822 and memory 1824 (e.g., volatile, non-volatile, etc.). One or moreeBooks 1822 may be stored in the memory 1824 and renderable by theprocessor 1822 on a display. The device 106 may further include variousreading support applications 1828 that aid during a reader's consumptionof the eBook. For instance, the support applications 1828 may be adictionary, a thesaurus, a highlighting tool, an annotation tool, and soforth. When the reader invokes one or more of the reading supportapplications 1828, the terms at the center of the activity are capturedand stored in a term file 1830 in association with the eBook from whichit was extracted and the identity of the reader and/or device 106. Thefile 1830 may then be passed to the servers 124(1)-(S), where it isstored in the vocabulary term database 1812.

In other implementations, the vocabulary question engine 302 may bestored and executed on the electronic device 106. In this way, thevocabulary questions are automatically generated locally based on theterms that the reader has indicated an interest. Once created, thevocabulary questions may then be stored in the memory 1824 and recycledfrom time-to-time.

Illustrative Operation

FIG. 19 shows a general process 1900 of accepting a query term andpresenting context-appropriate samples of the query term. The process1900 (as well as the process 2000 of FIG. 20) is illustrated as acollection of blocks in a logical flow graph, which represent a sequenceof operations that can be implemented in hardware, software, or acombination thereof. In the context of software, the blocks representcomputer-executable instructions that, when executed by one or moreprocessors, perform the recited operations. Generally,computer-executable instructions include routines, programs, objects,components, data structures, and the like that perform particularfunctions or implement particular abstract data types. The order inwhich the operations are described is not intended to be construed as alimitation, and any number of the described blocks can be combined inany order and/or in parallel to implement the process. For discussionpurposes, the process 1900 (as well as the process 2000 below) isdescribed with reference to the architectures and computing systemdescribed above.

At 1902, a term query relating to a term in a target passage of acontent item is accepted. In the architecture 100 of FIG. 1, forexample, the term query is captured by the electronic devices 106(1)-(D)as the reader selects.

At 1904, the contents of the content item and metadata are accessed. Asshown in FIG. 18, an eBook database 1810 may provide this informationabout the content item.

At 1906, the term query is associated with the content item. As shown inFIGS. 1 and 3, a data structure 126 may be implemented to associate theterm with the eBook, reader, and/or device.

At 1908, the term meaning is determined. The process of determining thequery term meaning is described in more detail next with regards to FIG.20. This determination may include analysis of the content item as wellas comparisons involving the metadata as shown in FIG. 2.

At 1910, one or more related portions of content items which use theterm in a substantially similar fashion as the target passage aredetermined. As discussed above in FIG. 14, these works may be chosen forhaving a definition which is consistent with that encountered within thetarget passage and also being within books relevant to the user.

At 1912, samples from the one or more related portions of content itemsare presented. For example, the UI depicted in FIG. 14 shows relevantexamples with the substantially similar or the same definition as usedin the target passage.

FIG. 20 is a flow diagram for a general process 2000 of determining ameaning of a selected term at least in part due to a correspondencebetween metadata of the content in which the selected term resides andthe metadata of the term.

At 2002, key terms in a target passage containing a query term aredetermined. These key terms may be individual terms, or a combination ofterms into a phrase.

At 2004, metadata relating to the content item containing the query termis accessed. As described above with respect to FIG. 2 this metadata mayinclude publication year, author, genre, comparable books, and so forth.

At 2006, possible meanings of the query term are accessed. This accessmay include querying the vocabulary information server 802.

At 2008, possible meanings are ranked according to correspondence ofmetadata associated with each meaning and the metadata of the contentitem. In some implementations, correspondence with metadata of othercontent items may also be incorporated. For example, a meaning which hasmetadata indicating its origin was during the 16^(th) century wouldreceive a higher ranking when the query originates from the electronicbook Romeo and Juliet published at about that time than when the queryoriginates from a more recent book such as The Art of the Deal.

At 2010, the highest ranking meaning of the query term is selected. Thisselected meaning may then be presented to the user, such as via theinterface of FIG. 14.

CONCLUSION

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described. Rather,the specific features and acts are disclosed as exemplary forms ofimplementing the claims.

1. A computer-implemented method, comprising: under control of one ormore computers configured with specific executable instructions,accepting a term query for a term in a target passage within anelectronic book; accessing contents of the electronic book and metadataassociated with the electronic book; associating the term query with theelectronic book; determining a top ranking meaning of the term from oneor more possible meanings, the top ranking meaning based at least inpart upon the contents of the electronic book and the metadataassociated with the electronic book; and determining one or more relatedportions of one or more other electronic books, the one or more relatedportions using the term consistent with the top ranking meaning in thetarget passage within the electronic book.
 2. The computer-implementedmethod of claim 1, further comprising presenting the one or more relatedportions.
 3. The computer-implemented method of claim 1, furthercomprising presenting the top ranking meaning of the term.
 4. Thecomputer-implemented method of claim 1, the determining the top rankingmeaning of the term further comprising: accessing the possible meaningsof the term; ranking the possible meanings according to correspondenceof metadata associated with the term query and the metadata associatedwith the electronic book; and designating a possible meaning of the termhaving a highest ranking as the top ranking meaning.
 5. Thecomputer-implemented method of claim 1, further comprising presenting aconfounder passage in which the term is used with a meaning other thanthe top ranking meaning, and presenting a question to a user as towhether the confound passage or the target passage is incorrect.
 6. Thecomputer-implemented method of claim 1, further comprising presenting areport displaying variation of meaning of the term within apre-determined set of parameters.
 7. The computer-implemented method ofclaim 6, wherein the pre-determined set of parameters comprise: a rangeof dates; a user demographic; genre; or a combination thereof.
 8. One ormore non-transitory computer-readable storage media storingcomputer-executable instructions that, when executed, cause one or moreprocessors to perform acts comprising: accepting a term query for a termin a target passage within an electronic book; accessing contents of theelectronic book and metadata associated with the electronic book;associating the term query with the electronic book; determining a topranking meaning of the term from one or more possible meanings, the topranking meaning based at least in part upon the contents of theelectronic book and the metadata associated with the electronic book;and determining one or more related portions of one or more otherelectronic books, the one or more related portions using the termconsistent with the top ranking meaning in the target passage within theelectronic book.
 9. The one or more non-transitory computer-readablestorage media of claim 8, further comprising presenting the one or morerelated portions.
 10. The one or more non-transitory computer-readablestorage media of claim 8, further comprising presenting the top rankingmeaning of the term.
 11. The one or more non-transitorycomputer-readable storage media of claim 8, the determining the topranking meaning of the term further comprising: accessing the possiblemeanings of the term; ranking the possible meanings according tocorrespondence of metadata associated with the term query and themetadata associated with the electronic book; and designating a possiblemeaning of the term having a highest ranking as the top ranking meaning.12. The one or more non-transitory computer-readable storage media ofclaim 8, further comprising presenting a confounder passage in which theterm is used with a meaning other than the top ranking meaning, andpresenting a question to a user as to whether the confound passage orthe target passage is incorrect.
 13. The one or more non-transitorycomputer-readable storage media of claim 8, further comprisingpresenting a report displaying variation of meaning of the term within apre-determined set of parameters.
 14. The one or more non-transitorycomputer-readable storage media of claim 13, wherein the pre-determinedset of parameters comprise: a range of dates; a user demographic; genre;or a combination thereof.
 15. A system comprising: one or moreprocessors; and one or more non-transitory computer-readable mediastoring computer-executable instructions that, when executed, cause theone or more processors to perform acts comprising: accepting a termquery for a term in a target passage within an electronic book;accessing contents of the electronic book and metadata associated withthe electronic book; associating the term query with the electronicbook; determining a top ranking meaning of the term from one or morepossible meanings, the top ranking meaning based at least in part uponthe contents of the electronic book and the metadata associated with theelectronic book; and determining one or more related portions of one ormore other electronic books, the one or more related portions using theterm consistent with the top ranking meaning in the target passagewithin the electronic book.
 16. The system of claim 15, furthercomprising presenting the one or more related portions.
 17. The systemof claim 15, further comprising presenting the determined top rankingmeaning of the term.
 18. The system of claim 15, the determining the topranking meaning of the term further comprising: accessing the possiblemeanings of the term; ranking the possible meanings according tocorrespondence of metadata associated with the term query and themetadata associated with the electronic book; and designating a possiblemeaning of the term having a highest ranking as the top ranking meaning.19. The system of claim 15, further comprising presenting a confounderpassage in which the term is used with a meaning other than the topranking meaning, and presenting a question to a user as to whether theconfound passage or the target passage is incorrect.
 20. The system ofclaim 15, further comprising presenting a report displaying variation ofmeaning of the term within a pre-determined set of parameters.
 21. Thesystem of claim 20, wherein the pre-determined set of parameterscomprise: a range of dates; a user demographic; genre; or a combinationthereof.